This application is for the development of a Center for Systems Biology to support research and education at the interface of systems engineering and genomics-based biomedical research. Systems engineering tools, methods and techniques will be modified and developed for these applications, and functional genomics datasets will be designed and created for these purposes. We propose to develop resources, datasets, tools and analysis results at this interface and to provide software engineering activities to maintain these. The application reflects an ongoing effort to formalize links between the Jefferson Medical College (JMC) and the University of Delaware Engineering College (UD). Initial activities between JMC and UD include planning for joint degree programs and formal research collaborations. The goal is to create computational biology infrastructure for an interdisciplinary, inter-institutional but tightly-integrated Computational Biology Program. These activities will take place in the context of interrelated Development Projects on the theme of crossing levels of analysis from molecular to systems physiology. All projects are characterized by combining systems engineering and genomics approaches and include IT infrastructure for sharing, maintaining and analyzing datasets. Developmental Project 1 develops novel computational approaches to describe differentiation at the gene and signaling network level. Developmental Project 2 takes statistical approaches to develop analysis of the gene clusters and gene co-regulation that describe a particular cell type?s molecular identity. We propose to develop an explicitly probabilistic scheme for the analysis of DNA microarray data, including sources of noise. Developmental Project 3 uses expression profiling as providing datasets that we will use in the systematic analysis of cellular physiology, which we believe is an exciting departure from common analyses of this kind of data. We also describe how novel uses of signal processing technologies can support computational modeling approaches to create a model of combinatorial control in the genetic regulatory networks for adaptive physiological responses.